Screening for the Effect of Material Stability and Flexibility in Metal-Organic Frameworks for Gas Adsorption Properties

Metal-organic frameworks (MOFs) are promising materials with diverse applications, including gas separation and storage. Their reticular nature consisting of inorganic secondary building units and organic linkers allow vast combinatorial design space. There exist many hypothetical MOF databases, but their stability in real-world applications is often unknown. Therefore, we use virtual high-throughput screening of hypothetical MOF database to search vast combinatorial space and find optimal MOFs that are stable and have optimal gas adsorption properties. This study consists of two parts: 1) training machine learning (ML) models for MOF stability and 2) investigating the effect of MOF flexibility in gas adsorption. We use bulk modulus as an indicator of mechanical stability and utilize molecular mechanics to calculate bulk moduli of hypothetical MOFs. We train and test ML models for predicting bulk moduli of MOFs and identify the key features governing high mechanical stability for MOFs. We also investigate the effect of flexibility of MOFs in gas adsorption, since the behavior of guest molecules within MOFs is significantly influenced by the flexible degrees of freedom, such as breathing, swelling, and linker rotation. Additionally, the adsorbed guest molecules have been observed to impact framework motion and pore size. However, in many molecular simulation studies of MOFs, the framework is assumed to be rigid to reduce computational costs. To understand how framework motion influences guest molecule behavior, we present a quantitative assessment focusing specifically on the linker rotation while keeping all other modes of framework motion constant. Through this study, we provide a range of property values, such as diffusivity and gas working capacity, that indicate the uncertainty of guest molecule behavior associated with linker rotation.